Some stats: - Total number of links: 24459

◄◄ Back to Wiki Index

For this we are going to be using lxc-create, which comes pre-installed on Proxmox but needs further dependencies to fetch the OCI images from Docker:

apt update && apt -y install skopeo umoci jq

あなたは Rust の学習を支援するメンターです。 ユーザーは Rust 初学者です。 構文だけでなく、 「なぜそのように書くのか」 「Rust がどんな考え方をするのか」 まで理解することを目指します。

You are an interactive CLI tool that helps users with software engineering tasks.

Write all English in ASD-STE100 Simplified Technical English. STE is a controlled language. The aerospace industry built it so that a reader who cannot ask a follow-up

Bu repo için AGENTS.md yaz. Amaç kural listesi değil: buraya yeni düşen bir kodlama ajanının (ve yeni ekip üyesinin) hangi hataları yapacağını önceden kesen bir harita.

AŞAMA 1 — Kanıt topla. Önce oku, sonra yaz. Uydurma yok.

• package.json / pyproject / go.mod / Gemfile: script'ler, paket yöneticisi, runtime sürümü pinlenmiş mi (.nvmrc, engines, volta gerçekten var mı?)
• Dizin ağacı (2 seviye). Aynı işi yapan iki klasör var mı? (api/ + apis/, navigations/ + routes/ gibi) — bunlar göç halinde, not al.
• CI dosyaları: gerçekte hangi komutlar çalışıyor, hangileri lokalde gereksiz.

• Copy content of vsix-bookmarklet, create a bookmark in your browser.
• Navigate to the web page of the VS Code extension you want to install.
• Click the bookmark you just created, then click the download button.
  
• After download finished, rename the file extension to *.vsix.
• In VS Code, select Install from VSIX... in the extension context menu.
  

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

• I. PRIVACY/SECURITY
• • ► VPNs
  • ► Adblockers & useful extensions
  • ► Useful userscripts
• II. TORRENT CLIENTS
• III. TORRENT INDEXERS/SITES
• IV. EBOOKS/TEXTBOOKS
• • ► Popular sites for books
  • ► Custom Search Engines
• ► Private trackers